Plants In Hungary Research Articles

Materials Challenges of the New Nuclear Power Plant in Hungary

Materials Challenges of the New Nuclear Power Plant in Hungary

Optimal place to apply post-processing in the deterministic photovoltaic power forecasting workflow

Is the post-processing of global horizontal irradiance (GHI) forecasts necessary for issuing good photovoltaic (PV) power forecasts? Whenever this question is raised, the instinctive supposition always seems to be “yes,” because GHI is the most important weather parameter governing the amount of PV power generated, and surely, the better the GHI forecasts are, the better the PV power forecasts should result. To attend to this question more scientifically and more formally, two classic deterministic-to-deterministic post-processing methods, namely, the model output statistics and kernel conditional density estimation, are applied at various stages of PV power forecasting, resulting in four distinct workflows. These different workflows are trained and tested on three PV plants in Hungary, using data from a four-year (2017–2020) period. Both ground-based GHI and satellite-retrieved GHI are used as the “truth” with which numerical weather prediction (NWP) GHI forecasts are post-processed. A very thorough deterministic forecast verification exercise is conducted following the best practices. It is found that contrary to the common supposition, post-processing GHI only leads to marginal, if that can be quantified at all, benefits, so long as the PV power forecasts are to be post-processed. This ought to be deemed as a very important finding, as it puts into question the “GHI forecasting + post-processing + irradiance-to-power conversion” workflow that has dominated solar forecasting for decades.

Microfiber emission from a municipal wastewater treatment plant in Hungary.

Since the ingestion of both natural and anthropogenic microfibers produces a deleterious effect on aquatic organisms, it is crucial to explore the emission of these pollutants by WWTPs into the receiving water bodies, such as rivers. Cellulose- and petroleum-based microfibers, as well as microplastic particles, were collected from the effluent of a municipal WWTP operating with activated sludge technology in Budapest, Hungary. During two sampling campaigns organized in February and April of 2023 on different working days and at different times of the day, 123-145 L of effluent was sieved and filtered. The organic matter was removed by hydrogen-peroxide treatment. All fibers and particles larger than 10µm were counted, and using a fluorescence microscope, the fibers were geometrically characterized in terms of length and diameter. Each fiber was individually identified by transflection-FT-IR method. The fiber concentration varied in the range of 1.88-2.84 and 4.25-6.79 items/L during the 7th and the 16th week of 2023, respectively. In February and April, the proportion of microfibers in the solid particles was 78.3 and 94.7%, respectively. In the effluent the cellulose-based microfibers were dominant (53-91%), while among the petroleum-based microfibers, polyester occurred most often. The median length of cellulose-based fibers was considerably higher in April than in February (650 vs. 1250µm), and simultaneously the median diameter also increased from 21 to 29µm. This behaviour was also seen, albeit to a lesser extent, in connection to microfibers derived from petroleum. The treated wastewater's daily microfiber transport to the Danube River varied between 0.44 -0.69 and 0.94-1.53 billion in February and April 2023, respectively.

Fundamental drying and agglomeration experiments with bio-fraction and refuse derived fuel for the development of pyrolysis reactor feed.

The EU's circular economy concept necessitates increasing the recycling ratio of municipal solid wastes. There are many existing mechanical-biological processing plants in Hungary for the preparation of residual municipal solid wastes (RMSWs). The two most important products of these plants are the bio-fraction and the refuse derived fuel (RDF). Currently, there are problems with both of these material streams in Hungary, since most of the bio-fraction is still landfilled, and the local thermal utilisation of the RDF has not been implemented yet. The high moisture content of the produced bio-fraction and RDF causes difficulties for the downstream operations; therefore, there is recent engineering interest in drying and agglomeration of these materials. The authors have carried out systematic and parallel drying and briquetting experimental series to study the effect of the material, material composition, mass (volume or surface) of the material, particle size distribution and pre-treatment with a cutting mill on drying intensity in a 1 m3 oven and their effect on briquettability by a laboratory briquette press. The initial slope of the relative moisture loss as function of time was determined. Process engineering design methods of convective hot air-drying can be further developed taking into account the research results. Results can be used for the design of the feed of a pyrolysis reactor once reactor experiments have provided the optimal feed requirements.

Sustainable approach for the collection and processing of medicinal and aromatic plants in Hungary

AbstractHungary is a Central European country that is rich in medicinal and aromatic wild plant species; in rural livelihoods, the collection, use, process, and trade of these plants are traditionally important contributors. However, due to several recent changes touching the sector, the natural ecosystems, biodiversity, and collectors - who generally belong to poorer social groups – are affected negatively.The paper aims to introduce the Hungarian herbal sector from a holistic perspective, including its economic, environmental, and human dimensions, with a particular focus on sustainability. In this context, the purpose of the article is to discover this field as comprehensibly as possible and present it from both theoretical and practical aspects. Another objective is to collect the best practices and feasible solutions from the field in connection with promoting a harmonious, as well as economically prosperous relationship between nature and local people. This integrated approach helps show the industry's strengths and advantages, as well as its weaknesses and challenges. Based on the findings, the paper attempts also to propose some recommendations for the future.

Comparing global and regional downscaled NWP models for irradiance and photovoltaic power forecasting: ECMWF versus AROME

Inspecting the literature, much effort has been placed on the verification of irradiance forecasts from numerical weather prediction (NWP) models, as such forecasts are thought to have profound implications on the photovoltaic (PV) power forecasts, which in turn affects grid operators' confidence in integrating such power into the electricity grid. However, perhaps due to the proprietary nature of PV plants and lack of access to state-of-the-art NWP model output, only few have had the chance to conduct head-to-head comparisons of global mesoscale and regional downscaled NWP models, in terms of how their irradiance forecast inaccuracies propagate to PV power forecasts. In this regard, this work presents such a study, in which irradiance and PV power forecasts from the European Centre for Medium-Range Weather Forecasts' High-Resolution (HRES) and Météo-France's Application of Research to Operations at Mesoscale (AROME) models are thoroughly verified against the ground-based measurements from 32 research-grade radiometry stations and 94 actual PV plants in Hungary. A wide range of techniques and case studies concerning verification is herein considered, including variance ratio analysis, Murphy–Winkler decomposition, point-versus-areal verification, and seasonal verification. Despite that the results are too numerous to be summarized in a few sentences, the overarching observation from the verification exercise is that the performance of irradiance forecasts can only be used to infer that of PV power forecasts to a certain extent, which contrasts the conventional wisdom.

Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting

Under the two-step framework of photovoltaic (PV) power forecasting, that is, forecasting first the irradiance and then converting it to PV power, there are two chief ways in which one can account for the uncertainty embedded in the final PV power forecast. One of those is to produce probabilistic irradiance forecast through, for example, ensemble numerical weather prediction (NWP), and the other is to pass the irradiance forecast through a collection of different irradiance-to-power conversion sequences, which are known as model chains. This work investigates, for the first time, into the question: Whether pairing ensemble NWP with ensemble model chain is better than leveraging any individual method alone? Using data from 14 utility-scale ground-mounted PV plants in Hungary and the state-of-the-art global mesoscale NWP model of the European Centre for Medium-Range Weather Forecasts, it is herein demonstrated that the best probabilistic PV power forecast needs to consider both ensemble NWP and ensemble model chain. Furthermore, owing to the higher-quality probabilistic forecasts, the point forecast accuracy is also improved substantially through pairing. Overall, the recommended paring strategy achieves a mean-normalized continuous ranked probability score and a root mean square error of 18.4% and 42.1%, respectively.

Probabilistic photovoltaic power forecasting using a calibrated ensemble of model chains

Physical model chain is a step-by-step modeling framework for the conversion of irradiance to photovoltaic (PV) power. When a model chain is fed with irradiance forecasts, it provides the corresponding PV power forecasts. Despite its advantages, forecasting with model chains has yet to receive the attention that it deserves. In several recent works, however, the idea of model-chain-based solar forecasting has been formally modernized, though the framework was restricted to deterministic forecasting. In this work, the model-chain-based forecasting framework is extended to the probability space, in that, a calibrated ensemble of model chains is used to generate probabilistic PV power forecasts. Using two-year data from eight PV plants in Hungary, alongside professional weather forecasts issued by the Hungarian Meteorological Services, it is empirically shown that the raw model-chain ensemble forecasts tend to be underdispered, but adequate post-processing is able to improve calibration and reduce the continuous ranked probability score of raw ensembles by 20%. Given the fact that uncertainty quantification has a cardinal importance to grid integration, this probabilistic extension of the model-chain-based solar forecasting framework is thought beneficial.

Benefits of physical and machine learning hybridization for photovoltaic power forecasting

Irradiance-to-power conversion is an essential step of state-of-the-art photovoltaic (PV) power forecasting, regardless of the source and post-processing of irradiance forecasts. The two distinct approaches for mapping the irradiance forecasts to PV power are physical and data-driven, which can also be hybridized. The contribution of this paper is twofold; first, it proposes a concept and identifies the best implementation of a hybrid physical and machine learning irradiance-to-power conversion method. Second, a head-to-head comparison of the physical, data-driven, and hybrid methods is performed for the operational day-ahead power forecasting of 14 PV plants in Hungary based on numerical weather prediction (NWP). To respect the rule of consistency but still obtain as complete picture as possible, two directives are set, namely minimizing the mean absolute error (MAE) and minimizing the root mean square error (RMSE), and separate sets of forecasts are optimized for both directives. The results reveal that for two years of training data, the hybrid method that involves the most physically-calculated predictors can reduce the MAE by 5.2% and 10.4% compared, respectively, to the optimized physical model chains and the machine learning without any physical considerations. The two most important physical modeling steps are separation and transposition modeling, and the rest of the physical PV simulation can be left to machine learning in hybrid models without a significant increase in the errors. The optimization of the physical model chains is found to be important even in the case of hybrid modeling; therefore, it should become a standard procedure in practical applications. Finally, the hybrid method is only beneficial for at least one year of training data, while in the initial period of the operation of a PV plant, it is advised to stay with optimized physical modeling. The guidelines and recommendations of this paper can help both researchers and practitioners design and optimize their power conversion model to increase the accuracy of the PV power forecasts.

Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction

The increase of the worldwide installed photovoltaic (PV) capacity and the intermittent nature of the solar resource highlights the importance of power forecasting for the grid integration of the technology. This study compares 24 machine learning models for deterministic day-ahead power forecasting based on numerical weather predictions (NWP), tested for two-year-long 15-min resolution datasets of 16 PV plants in Hungary. The effects of the predictor selection and the benefits of the hyperparameter tuning are also evaluated. The results show that the two most accurate models are kernel ridge regression and multilayer perceptron with an up to 44.6% forecast skill score over persistence. Supplementing the basic NWP data with Sun position angles and statistically processed irradiance values as the inputs of the learning models results in a 13.1% decrease of the root mean square error (RMSE), which underlines the importance of the predictor selection. The hyperparameter tuning is essential to exploit the full potential of the models, especially for the less robust models, which are prone to under or overfitting without proper tuning. The overall best forecasts have a 13.9% lower RMSE compared to the baseline scenario of using linear regression. Moreover, the power forecasts based on only daily average irradiance forecasts and the Sun position angles have only a 1.5% higher RMSE than the best scenario, which demonstrates the effectiveness of machine learning even for limited data availability. The results of this paper can support both researchers and practitioners in constructing the best data-driven techniques for NWP-based PV power forecasting.

Aspects of Determining the Energy Storage System Size Linked to Household-Sized Power Plants in Hungary in Accordance with the Regulatory Needs of the Electric Energy System

The global energy markets of the last decade have been characterized by an ever-increasing share of electric power, more than half of which is projected to come from renewable energy sources by the year 2030. Such a remarkable rise in the quantity of renewable energy, of course, will induce a series of related changes as, without the successful integration of all that unconventional type of energy into the existing energy systems, the sustainability and security of the electricity supply cannot be maintained. As a result, new legislation and energy policies are required all over the world to accommodate not only the latest technological solutions but also a variety of previously unknown market actors. In the institutions, businesses and households of Hungary, the notion of sustainability has been gaining more and more importance lately, which is manifest in the efforts to reduce the use of electricity from the public grid, which is generated by burning fossil fuel. This endeavor is facilitated by the installation of photovoltaic (PV) household-sized power plant (HMKE) systems. Currently, the Hungarian electric energy system does not possess sufficiently flexible capacities; moreover, even this capacity is expected to decrease considerably in the future due to the phasing out fossil fuel power plants. Furthermore, dynamically growing HMKE penetration means an increasing frequency of technical problems in the macroenergy system (e.g., reverse energy flow in the local grid). It is such challenges that energy storage technologies can provide a solution for. Presently, there is insufficient information available on the recommended energy storage size necessary for the efficient integration of Hungarian HMKE systems into the electric energy system and the related investment needs. The innovative novelty of this study is that it examines the quantity and power of Hungarian HMKEs in the districts of the various electric companies over time with a view of exploring a possible way of their efficient integration into the electric energy system by determining the nominal energy storage power and energy capacity of the proposed energy storage systems. In addition, the paper also presents the expected investment needs associated with these energy storage systems.

Total Saponin Content (TSC) of different alfalfa (Medicago sativa L.) cultivars cultivated in field experiment

Saponins are secondary metabolites produced by various plants. These compounds have important role in the defence system. The word saponin refers to a group of different chemical compounds. Basically, sugar conjugates of triterpenoids or steroids are called saponins. Triterpene-type saponins are more specific among dicotyledonous plants, while steroid-type saponins are more characteristic of plants belonging to the monocotyledonous taxonomic group. Alfalfa is a large-scale cultivated and foraged fodder plant in Hungary. In the defence mechanism of alfalfa, saponins also play an important role. However, large amount of saponins can be toxic in animal fodder, especially in the poultry farming and piggery. As a dicotyledonous plant, the alfalfa saponins are mainly triterpenoid type. In our study we measure the total triterpenoid saponin content and leaf stem ratio of field cultivated alfalfa cultivars. Samples were collected from a randomized block design experiment, planted in the Demonstration Garden and Arboretum of Institutes of Agricultural Research and Educational Farm, in Debrecen, in 2018. Three different cultivar were investigated, and the samples were collected three different times of the growing season at growing stage of early flowering, for three years (2018-2020.). There was no difference in the TSC of examined varieties on the average of all measured data, although the sampling time showed significant effect on TSC. Our results attract attention to the fact that ageing of alfalfa stands can cause increase in TSC.

Waste Heat Recovery of Tura Geothermal Excess Steam Using Organic Rankine Cycle

A developing geothermal utilization is one of many Hungarian government efforts to generate electricity and heating applications from renewable energy sector, and to reduce fossil fuels usage due to the impact on the environment. Geothermal utilization for electricity generation has been implemented in Tura region and it is become the first geothermal plant in Hungary that producing electricity around 27 MW. The excess steam from Tura geothermal power-plant still has a potential energy that can converted to electricity and the objection in this study is implemented a heat recovery from excess steam through organic Rankine cycle (ORC) from the point of view energy and exergy analysis using different working fluids. The calculation result shows the Propane is produce the highest energy around 41 kW and the efficiency at 10.3%, while R125 produce the lowest energy around 10.25 kW and the efficiency at 8.17%. Moreover, based on the environmental analysis it is also found that R134a working fluid can be considered as environmentally and sustainability ORC’s working fluid, compared to other working fluids, in this study.

Known but not called by name: recreational fishers\u2019 ecological knowledge of freshwater plants in Hungary

BackgroundDocumenting local ecological knowledge (LEK) has recently become a topic of considerable interest. LEK can contribute to various areas of ecology, including habitat management and conservation biology. It has been recently revealed that recreational fishers’ ecological knowledge (FEK) can also provide valuable information about different organisms and habitats, while recreational fishers’ ecological knowledge is understudied in many aspects and regions of the world.MethodsWe aimed to record Hungarian recreational FEK on plant species related to freshwater habitats. Our research was conducted in three regularly fished water bodies in Hungary, namely Lake Velence, Keleti Main Canal, and Lake Látóképi, where a total of 72 interviews were conducted with recreational anglers. During interviews, 24 plant species occurring at freshwater habitats with common or sporadic distribution were shown to anglers as single species or in congeneric pairs. Miscellaneous plant-related knowledge of anglers was also collected.ResultsAnglers identified a total of 16 plant species. They used 45 botanical or folk names. An angler knew the name of 4.6 plants on average and recognized 7.4 other species without naming it. According to our detailed analysis, anglers were able to name or at least recognize those plant species which are somehow related to fishing activities, are salient, and/or common. Moreover, anglers at Lake Velence recognized less plant species; however, they also had less years of fishing experience compared to anglers of the other two locations.ConclusionWe found that recreational FEK exists even in the case of freshwater plants which are not the main focus of anglers. It is highly presumable that recreational fishers would be able to provide reliable ecologically related data for scientific research establishing future citizen science projects of nature conservation.

Influence of design data availability on the accuracy of physical photovoltaic power forecasts

Accurate photovoltaic (PV) power forecasts are essential for the grid integration of the technology. Most solar forecasting studies deal only with solar irradiance forecasting, which highlights the importance of the irradiance to power conversion methods to enable the practical utilization of the advanced irradiance forecasting techniques. The PV power output is commonly calculated with physical model chains that rely on the main design parameters of the PV plants. This study presents an analysis of how the unavailability or uncertain knowledge of the design parameters affects the physical power forecast accuracy. The analysis is performed by verifying the power forecasts created by the bests of 32,400 model chains for 16 PV plants in Hungary for five data availability scenarios based on numerical weather prediction data. The results show that even though the knowledge of all relevant design parameters ensures the lowers errors, the critical parameters are only the nameplate capacities and the module orientation. The increase in mean absolute error and root mean square error is only 1.3% and 0.5%, respectively, if only these crucial parameters are known compared to the baseline scenario. If necessary, the tilt and azimuth angles can also be inferred, where the resulting inaccuracy depends on the estimation errors. The results reveal that the physical model chains can be used with a decent accuracy for power forecast calculations even if the design parameters are unknown, which highly broadens the applicability of the physical method.

Examination of the effect of pelleted poultry manure products on a sunflower test plant in a laboratory model experiment

Sunflower (Helianthus annuus L.) is one of the most important oil plants in Hungary, with a sown area of more than 627.000 ha in 2019. Sunflower cultivation is primarily important for use in the oil industry, but its role in feed and beekeeping cannot be neglected either. Sunflower adapts well to different production areas in terms of soil demand, yet, as important fact, it must be grown on soils with poorer conditions with intensive nutrient replenishment. From the agri-environmental point of view, in addition to the use of pelleted poultry manure products, which are also new to the consumer market, is of paramount importance. Their advantages include a wide range of nutrients as well as a soil structure improving effect. In our research, we investigated the starter effect of pelleted poultry manure products applied at different doses (200 kg ha-1 and 400 kg ha-1) on sandy soil with humus using a sunflower plant. The aim of our research to investigate the effect of using pelleted fertilizer products as starter fertilizers on sunflower plant germination, and investigate the effect of poultry manure products on soils. Based on our results, it can be stated that the pigment content (chlorophyll and carotenoid content) was positively influenced by the higher dose treatments (400 kg ha-1), thus increasing the photosynthetically active leaf area. Compared to the control, the carotenoid content in the test plants of the treatments increased, and the differences of this treatment proved to be significant (p<0.05). Our experiments supported the beneficial effect of pelleted poultry manure-based products in the soil-plant system.

Zebeeba falsalis (Herrich-Schäffer, 1839) (Erebidae, Rivulinae) magyarországi előfordulása

Zebeeba falsalis (Herrich-Schäffer, 1839) has a Mediterranean distribution. In Hungary it was not recorded until 2020. One male specimen was attracted to light on Zöld-hegy near Csákvár in the Vértes Mountains. This species feeds on Asparagus plants, mainly on Asparagus acutifolius in the Mediterranean region. Presumably, its host plant in Hungary is Asparagus officinalis. Its occurrence is due to the climatic changes in recent years.

Relationships between the Number and Power of Hungarian Household-Sized Photovoltaic Power Plants and Selected Indicators of the Settlements: A Case Study

In the global transformation of energy systems, solar energy plays a prominent role, since the energy from our star is a limitless and clean resource, which is available practically almost everywhere. In spite of the immense advancements of photovoltaic systems, which utilize this source of energy, no in-depth research has been carried out regarding the present Hungarian status of the small-scale photovoltaic power plants, the most common type of solar power plant in Hungary. The novelty of this study is that it examines the number and power of these small-scale power plants at the settlement level within the service areas of the various distribution companies, by also considering the economic and infrastructural dimensions of the settlements. The paper seeks answers to the questions whether there are any significant relationships between the number and the power of power plants of this type and the indicators of the settlements, and if so, how strong they are. Besides pairwise correlations, the study also involved the analysis of the relationship between the ranking of the settlements based on the settlements’ complex indicators that were created from the settlements’ indicators and the ranking of the settlements according to the number and power of household-sized photovoltaic power plants per 1000 people. In the course of exploring the relationships, a regression model was also devised concerning the number of household-sized photovoltaic power plants and the settlement indicators.

Extensive comparison of physical models for photovoltaic power forecasting

Forecasting the power production of grid-connected photovoltaic (PV) power plants is essential for both the profitability and the prospects of the technology. Physically inspired modelling represents a common approach in calculating the expected power output from numerical weather prediction data. The model selection has a high effect on physical PV power forecasting accuracy, as the difference between the most and least accurate model chains is 13% in mean absolute error (MAE), 12% in root mean square error (RMSE), and 23–33% in skill scores for a PV plant on average. The power forecast performance analysis performed and verified for one-year 15-min resolution production data of 16 PV plants in Hungary for day-ahead and intraday time horizons on all possible combinations of nine direct and diffuse irradiance separation, ten tilted irradiance transposition, three reflection loss, five cell temperature, four PV module performance, two shading loss, and three inverter models.The two most critical calculation steps are identified as irradiance separation and transposition modelling, while the inverter models are the least important. Absolute and squared errors are two conflicting metrics, as the more detailed models result in the lowest MAE, while the simplest ones have the lowest RMSE. Wind speed forecasts have only a marginal effect on the PV power prediction. The results of this study contribute to a deeper understanding of the physical forecasting approach in the research community, while the main conclusions are also beneficial for PV plant owners in preparing their generation forecasts.

The Potential Role of Power-to-Gas Technology Connected to Photovoltaic Power Plants in the Visegrad Countries—A Case Study

With the spread of the use of renewable sources of energy, weather-dependent solar energy is also coming more and more to the fore. The quantity of generated electric power changes proportionally to the intensity of solar radiation. Thus, a cloudy day, for example, greatly reduces the amount of electricity produced from this energy source. In the countries of the European Union solar power plants are obligated to prepare power generation forecasts broken down to 15- or 60-min intervals. The interest of the regionally responsible transmission system operators is to be provided with forecasts with the least possible deviation from the actual figures. This paper examines the Visegrad countries’ intraday photovoltaic forecasts and their deviations from real power generation based on the photovoltaic power capacity monitored by the transmission system operators in each country. The novelty of this study lies in the fact that, in the context of monitored PV capacities in the Visegrad countries, it examines the regulation capacities needed for keeping the forecasts. After comparing the needs for positive and negative regulation, the author made deductions regarding storage possibilities complementing electrochemical regulation, based on the balance. The paper sought answers concerning the technologies required for the balancing of PV power plants in the examined countries. It was established that, as a result of photovoltaic power capacity regulation, among the four Visegrad countries, only the Hungarian transmission system operator has negative required power regulation, which could be utilized in power-to-gas plants. This power could be used to produce approximately 2.1 million Nm3 biomethane with a 98% methane content, which could be used to improve approximately 4 million Nm3 biogas of poor quality by enriching it (minimum 60% methane content), so that it can be utilized. The above process could enhance the viability of 4–6 low-methane agricultural biogas plants in Hungary.